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Scheda Riassuntiva
Anno Accademico 2018/2019
Scuola Scuola di Ingegneria Industriale e dell'Informazione
Insegnamento 095843 - MEASUREMENTS
Docente Manzoni Stefano
Cfu 5.00 Tipo insegnamento Monodisciplinare

Corso di Studi Codice Piano di Studio preventivamente approvato Da (compreso) A (escluso) Insegnamento
Ing Ind - Inf (Mag.)(ord. 270) - BV (483) MECHANICAL ENGINEERING - INGEGNERIA MECCANICA*AE095843 - MEASUREMENTS

Obiettivi dell'insegnamento

The course provides a comprehensive summa of measurement and processing techniques of mechanical and thermal phenomena and enables students to perform the digital acquisition, the processing of the signals and the identification and the diagnostics of the main parameters of such systems. Compared to basic measurenent courses, this results more focused on signal analysis and processing algorithms in the time and frequency domains (base and advanced  frequency analysis, frequency response functions, multichannel analysis basic and advanced statistics). Analogic and digital conditioning techniques, data transmission, storage and compression are dealt with as well.

The students will learn the theory behind digital acquisition and digital signal processing. Furthermore, the students will learn how to choose and apply properly in practical applications the methods and algorithms learnt in the course.


Risultati di apprendimento attesi

The students will learn the basics of:

- digital signal acquisition;

- digital signal processing in the time domain

- digital signal processing in frequency domain

 

Moreover, the students will learn the theoretical relationships existing between time and frequency domains.

 

The students will learn to choose and apply correctly the different methods discussed in the course and to choose properly the processing strategy in different practical situations.

 

More in details, at the end of the teaching, the student is expected to reach the following learning outcomes:

Knowledge and comprehension

  • Data acquisition
  • Analysis of signals in time domain
  • Analysis of signals in frequency domain

Ability in the application of knowledge

  • Identify the correct data acquisition and data analysis techniques for signal processing
  • Avoid leakage and aliasing errors
  • Estimate the transfer function in SISO systems

Authonomy in judgement

  • select the best signal processing technique for evidencing machine misfunctions
  • compare signals

 

All the competences listed above will be developed relying on the theoretical lectures and on the laboratory experiences, that are crucial for a deep understanding of the most common problems that have to be faced during the mechanical engineer working life.


Argomenti trattati

Description of topics
Signals in the time domain
Classification of signals and their features: stationary and non-stationary signals, random and deterministic signals.
Analysis in the time domain: statistical parameters and correlation
Basic and time-varying statistical parameters.
Correlation functions.
Advanced techniques for data acquisition
Recall of the basic about signal acquisition. Aliasing and different acquisition strategies and high-level sampling techniques.
The convolution integral and the theorem of convolution
Dirac function, impulse response
The convolution integral and the theorem of convolution
Frequency response functions
Signal analysis in the frequency domain
Band analysis, Fourier-direct and inverse transform,  frequency resolution, leakage, windowing techniques.
Characterisation of systems in the frequency domain: spectra, power-spectra, cross-spectra, coherence function, estimation of the frequency response functiona,
Hilbert transform, cepstrum, time-frequency transform

Fieldbus
Data Transmission, digital communication, storage and data compression. Big data introduction.

 

 


Prerequisiti

Students are expected to have rooted bases in mechanical and thermal measurements, measurement chain design, mechanical system dynamics, and mathematics.


Modalità di valutazione

The attendance to the lectures and labs is not mandatory, but warmly suggested.

The exam is composed by a written and an oral part. Each single teacher can propose mid term tests.

Alternatively (teacher-dependent), it could be a discussion about activities carried out by the student during the course.

 

The questions at both written and oral exams can be related to the contents of both lectures and labs.

 

In the written part, the student is expected to show a strong theoretical knowledge related to the course topics. Moreover, he/she has also to show a strong capability to choose and apply correctly the methods/algorithms in given scenarios both through exercises and theoretical questions based on simulated scenarios.

 

In the oral part, specific parts of the program are deepened by means of both theoretical questions and exercises. The student is expected to show the capability of managing different topics together and the ability of linking them in given cases.

The mark of the written test is the starting point for the oral test, where the mark can be increased/decreased according to the student's performance.


Bibliografia

Forme didattiche
Tipo Forma Didattica Ore di attività svolte in aula
(hh:mm)
Ore di studio autonome
(hh:mm)
Lezione
27:30
41:15
Esercitazione
2:30
3:45
Laboratorio Informatico
20:00
30:00
Laboratorio Sperimentale
0:00
0:00
Laboratorio Di Progetto
0:00
0:00
Totale 50:00 75:00

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese
schedaincarico v. 1.6.5 / 1.6.5
Area Servizi ICT
27/09/2020